Overview

Dataset statistics

Number of variables24
Number of observations561
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory635.8 KiB
Average record size in memory1.1 KiB

Variable types

Categorical14
DateTime2
Numeric7
Unsupported1

Alerts

N_VICTIMAS has constant value "1"Constant
ID has a high cardinality: 561 distinct valuesHigh cardinality
LUGAR_DEL_HECHO has a high cardinality: 553 distinct valuesHigh cardinality
Calle has a high cardinality: 251 distinct valuesHigh cardinality
Dirección Normalizada has a high cardinality: 527 distinct valuesHigh cardinality
XY (CABA) has a high cardinality: 509 distinct valuesHigh cardinality
pos x has a high cardinality: 508 distinct valuesHigh cardinality
pos y has a high cardinality: 508 distinct valuesHigh cardinality
AAAA is highly overall correlated with POBLACIÓN AL AÑOHigh correlation
POBLACIÓN AL AÑO is highly overall correlated with AAAAHigh correlation
PARTICIPANTES is highly overall correlated with VICTIMA and 2 other fieldsHigh correlation
VICTIMA is highly overall correlated with PARTICIPANTES and 1 other fieldsHigh correlation
ACUSADO is highly overall correlated with PARTICIPANTESHigh correlation
ROL is highly overall correlated with PARTICIPANTES and 1 other fieldsHigh correlation
ID is uniformly distributedUniform
LUGAR_DEL_HECHO is uniformly distributedUniform
Dirección Normalizada is uniformly distributedUniform
XY (CABA) is uniformly distributedUniform
pos x is uniformly distributedUniform
pos y is uniformly distributedUniform
ID has unique valuesUnique
HORA is an unsupported type, check if it needs cleaning or further analysisUnsupported
HH has 15 (2.7%) zerosZeros

Reproduction

Analysis started2023-09-08 04:01:00.997111
Analysis finished2023-09-08 04:01:05.961802
Duration4.96 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

ID
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct561
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size40.5 KiB
2016-0001
 
1
2019-0077
 
1
2019-0070
 
1
2019-0072
 
1
2019-0073
 
1
Other values (556)
556 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters5049
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique561 ?
Unique (%)100.0%

Sample

1st row2016-0001
2nd row2016-0002
3rd row2016-0003
4th row2016-0005
5th row2016-0008

Common Values

ValueCountFrequency (%)
2016-0001 1
 
0.2%
2019-0077 1
 
0.2%
2019-0070 1
 
0.2%
2019-0072 1
 
0.2%
2019-0073 1
 
0.2%
2019-0074 1
 
0.2%
2019-0075 1
 
0.2%
2019-0076 1
 
0.2%
2019-0078 1
 
0.2%
2019-0068 1
 
0.2%
Other values (551) 551
98.2%

Length

2023-09-07T23:01:06.008081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016-0001 1
 
0.2%
2016-0015 1
 
0.2%
2016-0025 1
 
0.2%
2016-0021 1
 
0.2%
2016-0020 1
 
0.2%
2016-0019 1
 
0.2%
2016-0017 1
 
0.2%
2016-0016 1
 
0.2%
2016-0013 1
 
0.2%
2016-0028 1
 
0.2%
Other values (551) 551
98.2%

Most occurring characters

ValueCountFrequency (%)
0 1761
34.9%
2 837
16.6%
1 730
14.5%
- 561
 
11.1%
8 221
 
4.4%
7 212
 
4.2%
6 190
 
3.8%
9 178
 
3.5%
3 122
 
2.4%
5 122
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4488
88.9%
Dash Punctuation 561
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1761
39.2%
2 837
18.6%
1 730
16.3%
8 221
 
4.9%
7 212
 
4.7%
6 190
 
4.2%
9 178
 
4.0%
3 122
 
2.7%
5 122
 
2.7%
4 115
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5049
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1761
34.9%
2 837
16.6%
1 730
14.5%
- 561
 
11.1%
8 221
 
4.4%
7 212
 
4.2%
6 190
 
3.8%
9 178
 
3.5%
3 122
 
2.4%
5 122
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5049
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1761
34.9%
2 837
16.6%
1 730
14.5%
- 561
 
11.1%
8 221
 
4.4%
7 212
 
4.2%
6 190
 
3.8%
9 178
 
3.5%
3 122
 
2.4%
5 122
 
2.4%

N_VICTIMAS
Categorical

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size36.2 KiB
1
561 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters561
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 561
100.0%

Length

2023-09-07T23:01:06.084662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-07T23:01:06.153405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 561
100.0%

Most occurring characters

ValueCountFrequency (%)
1 561
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 561
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 561
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 561
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 561
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 561
100.0%

FECHA
Date

Distinct501
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
Minimum2016-01-01 00:00:00
Maximum2021-12-30 00:00:00
2023-09-07T23:01:06.224345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:06.329387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AAAA
Real number (ℝ)

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.4064
Minimum2016
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2023-09-07T23:01:06.416046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2018
Q32020
95-th percentile2021
Maximum2021
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6449799
Coefficient of variation (CV)0.00081498941
Kurtosis-1.1230852
Mean2018.4064
Median Absolute Deviation (MAD)1
Skewness0.18309098
Sum1132326
Variance2.7059587
MonotonicityIncreasing
2023-09-07T23:01:06.476853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2018 125
22.3%
2017 109
19.4%
2021 91
16.2%
2019 88
15.7%
2016 80
14.3%
2020 68
12.1%
ValueCountFrequency (%)
2016 80
14.3%
2017 109
19.4%
2018 125
22.3%
2019 88
15.7%
2020 68
12.1%
2021 91
16.2%
ValueCountFrequency (%)
2021 91
16.2%
2020 68
12.1%
2019 88
15.7%
2018 125
22.3%
2017 109
19.4%
2016 80
14.3%

MM
Real number (ℝ)

Distinct12
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5811052
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2023-09-07T23:01:06.537879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5977572
Coefficient of variation (CV)0.54667979
Kurtosis-1.2504951
Mean6.5811052
Median Absolute Deviation (MAD)3
Skewness-0.027269398
Sum3692
Variance12.943857
MonotonicityNot monotonic
2023-09-07T23:01:06.597036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 59
10.5%
12 59
10.5%
11 55
9.8%
8 52
9.3%
5 51
9.1%
6 49
8.7%
2 44
7.8%
7 42
7.5%
10 42
7.5%
3 39
7.0%
Other values (2) 69
12.3%
ValueCountFrequency (%)
1 59
10.5%
2 44
7.8%
3 39
7.0%
4 37
6.6%
5 51
9.1%
6 49
8.7%
7 42
7.5%
8 52
9.3%
9 32
5.7%
10 42
7.5%
ValueCountFrequency (%)
12 59
10.5%
11 55
9.8%
10 42
7.5%
9 32
5.7%
8 52
9.3%
7 42
7.5%
6 49
8.7%
5 51
9.1%
4 37
6.6%
3 39
7.0%

DD
Real number (ℝ)

Distinct31
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.777184
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2023-09-07T23:01:06.664560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.5589498
Coefficient of variation (CV)0.54248908
Kurtosis-1.1051637
Mean15.777184
Median Absolute Deviation (MAD)7
Skewness-0.010298032
Sum8851
Variance73.255621
MonotonicityNot monotonic
2023-09-07T23:01:06.733911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20 26
 
4.6%
17 26
 
4.6%
11 23
 
4.1%
14 23
 
4.1%
23 22
 
3.9%
15 22
 
3.9%
19 21
 
3.7%
3 21
 
3.7%
22 21
 
3.7%
12 20
 
3.6%
Other values (21) 336
59.9%
ValueCountFrequency (%)
1 16
2.9%
2 17
3.0%
3 21
3.7%
4 18
3.2%
5 15
2.7%
6 15
2.7%
7 20
3.6%
8 12
2.1%
9 20
3.6%
10 18
3.2%
ValueCountFrequency (%)
31 11
2.0%
30 14
2.5%
29 15
2.7%
28 19
3.4%
27 19
3.4%
26 16
2.9%
25 16
2.9%
24 15
2.7%
23 22
3.9%
22 21
3.7%

HORA
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size32.0 KiB

HH
Real number (ℝ)

Distinct24
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.841355
Minimum0
Maximum23
Zeros15
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2023-09-07T23:01:06.928610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median11
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.6047711
Coefficient of variation (CV)0.55777158
Kurtosis-1.1329227
Mean11.841355
Median Absolute Deviation (MAD)5
Skewness0.027217555
Sum6643
Variance43.623001
MonotonicityNot monotonic
2023-09-07T23:01:06.991719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
7 33
 
5.9%
6 31
 
5.5%
14 30
 
5.3%
10 28
 
5.0%
8 28
 
5.0%
9 28
 
5.0%
5 28
 
5.0%
18 27
 
4.8%
19 26
 
4.6%
21 25
 
4.5%
Other values (14) 277
49.4%
ValueCountFrequency (%)
0 15
2.7%
1 19
3.4%
2 12
 
2.1%
3 20
3.6%
4 17
3.0%
5 28
5.0%
6 31
5.5%
7 33
5.9%
8 28
5.0%
9 28
5.0%
ValueCountFrequency (%)
23 23
4.1%
22 24
4.3%
21 25
4.5%
20 19
3.4%
19 26
4.6%
18 27
4.8%
17 23
4.1%
16 23
4.1%
15 20
3.6%
14 30
5.3%

LUGAR_DEL_HECHO
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct553
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size52.8 KiB
AV AMANCIO ALCORTA Y BONAVENA
 
2
CANTILO, INT. Y UDAONDO, GUILLERMO AV.
 
2
Rivadavia Av. y Pedernera
 
2
AV 27 DE FEBRERO Y AV ESCALADA
 
2
Nazca Av. y Rivadavia Av.
 
2
Other values (548)
551 

Length

Max length82
Median length51
Mean length28.666667
Min length8

Characters and Unicode

Total characters16082
Distinct characters75
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique545 ?
Unique (%)97.1%

Sample

1st rowAV PIEDRA BUENA Y AV FERNANDEZ DE LA CRUZ
2nd rowAV GRAL PAZ Y AV DE LOS CORRALES
3rd rowAV ENTRE RIOS 2034
4th rowAV SAN JUAN Y PRESIDENTE LUIS SAENZ PEÑA
5th rowAV 27 DE FEBRERO Y AV ESCALADA

Common Values

ValueCountFrequency (%)
AV AMANCIO ALCORTA Y BONAVENA 2
 
0.4%
CANTILO, INT. Y UDAONDO, GUILLERMO AV. 2
 
0.4%
Rivadavia Av. y Pedernera 2
 
0.4%
AV 27 DE FEBRERO Y AV ESCALADA 2
 
0.4%
Nazca Av. y Rivadavia Av. 2
 
0.4%
AV. INDEPENDENCIA Y VIRREY CEVALLOS 2
 
0.4%
PAZ, GRAL. AV. Y DEL LIBERTADOR AV. 2
 
0.4%
SAN PEDRITO AV. Y DIRECTORIO AV. 2
 
0.4%
ALBERDI, JUAN BAUTISTA AV. Y AZUL 1
 
0.2%
ALVAREZ THOMAS AV. 1788 1
 
0.2%
Other values (543) 543
96.8%

Length

2023-09-07T23:01:07.081055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
av 494
 
16.5%
y 427
 
14.2%
de 100
 
3.3%
gral 71
 
2.4%
paz 49
 
1.6%
juan 38
 
1.3%
la 33
 
1.1%
au 32
 
1.1%
del 27
 
0.9%
san 25
 
0.8%
Other values (693) 1704
56.8%

Most occurring characters

ValueCountFrequency (%)
2453
 
15.3%
A 1823
 
11.3%
E 845
 
5.3%
R 812
 
5.0%
O 730
 
4.5%
L 589
 
3.7%
. 581
 
3.6%
I 579
 
3.6%
N 552
 
3.4%
V 515
 
3.2%
Other values (65) 6603
41.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10027
62.3%
Space Separator 2453
 
15.3%
Lowercase Letter 2260
 
14.1%
Other Punctuation 812
 
5.0%
Decimal Number 520
 
3.2%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Control 2
 
< 0.1%
Dash Punctuation 1
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 334
14.8%
e 254
11.2%
r 212
9.4%
o 189
 
8.4%
v 156
 
6.9%
n 153
 
6.8%
i 146
 
6.5%
l 123
 
5.4%
y 115
 
5.1%
s 91
 
4.0%
Other values (20) 487
21.5%
Uppercase Letter
ValueCountFrequency (%)
A 1823
18.2%
E 845
 
8.4%
R 812
 
8.1%
O 730
 
7.3%
L 589
 
5.9%
I 579
 
5.8%
N 552
 
5.5%
V 515
 
5.1%
T 406
 
4.0%
D 405
 
4.0%
Other values (17) 2771
27.6%
Decimal Number
ValueCountFrequency (%)
0 76
14.6%
1 76
14.6%
2 71
13.7%
5 63
12.1%
3 49
9.4%
9 42
8.1%
4 42
8.1%
7 37
7.1%
6 34
6.5%
8 30
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 581
71.6%
, 231
 
28.4%
Space Separator
ValueCountFrequency (%)
2453
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Control
ValueCountFrequency (%)
2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12287
76.4%
Common 3795
 
23.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1823
 
14.8%
E 845
 
6.9%
R 812
 
6.6%
O 730
 
5.9%
L 589
 
4.8%
I 579
 
4.7%
N 552
 
4.5%
V 515
 
4.2%
T 406
 
3.3%
D 405
 
3.3%
Other values (47) 5031
40.9%
Common
ValueCountFrequency (%)
2453
64.6%
. 581
 
15.3%
, 231
 
6.1%
0 76
 
2.0%
1 76
 
2.0%
2 71
 
1.9%
5 63
 
1.7%
3 49
 
1.3%
9 42
 
1.1%
4 42
 
1.1%
Other values (8) 111
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16052
99.8%
None 30
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2453
 
15.3%
A 1823
 
11.4%
E 845
 
5.3%
R 812
 
5.1%
O 730
 
4.5%
L 589
 
3.7%
. 581
 
3.6%
I 579
 
3.6%
N 552
 
3.4%
V 515
 
3.2%
Other values (58) 6573
40.9%
None
ValueCountFrequency (%)
Ñ 12
40.0%
ó 6
20.0%
ñ 4
 
13.3%
á 4
 
13.3%
é 2
 
6.7%
° 1
 
3.3%
í 1
 
3.3%

TIPO_DE_CALLE
Categorical

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size39.4 KiB
AVENIDA
357 
CALLE
114 
AUTOPISTA
47 
GRAL PAZ
43 

Length

Max length9
Median length7
Mean length6.8377897
Min length5

Characters and Unicode

Total characters3836
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAVENIDA
2nd rowGRAL PAZ
3rd rowAVENIDA
4th rowAVENIDA
5th rowAVENIDA

Common Values

ValueCountFrequency (%)
AVENIDA 357
63.6%
CALLE 114
 
20.3%
AUTOPISTA 47
 
8.4%
GRAL PAZ 43
 
7.7%

Length

2023-09-07T23:01:07.233444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-07T23:01:07.369904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
avenida 357
59.1%
calle 114
 
18.9%
autopista 47
 
7.8%
gral 43
 
7.1%
paz 43
 
7.1%

Most occurring characters

ValueCountFrequency (%)
A 1008
26.3%
E 471
12.3%
I 404
10.5%
N 357
 
9.3%
D 357
 
9.3%
V 357
 
9.3%
L 271
 
7.1%
C 114
 
3.0%
T 94
 
2.5%
P 90
 
2.3%
Other values (7) 313
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3793
98.9%
Space Separator 43
 
1.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1008
26.6%
E 471
12.4%
I 404
10.7%
N 357
 
9.4%
D 357
 
9.4%
V 357
 
9.4%
L 271
 
7.1%
C 114
 
3.0%
T 94
 
2.5%
P 90
 
2.4%
Other values (6) 270
 
7.1%
Space Separator
ValueCountFrequency (%)
43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3793
98.9%
Common 43
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1008
26.6%
E 471
12.4%
I 404
10.7%
N 357
 
9.4%
D 357
 
9.4%
V 357
 
9.4%
L 271
 
7.1%
C 114
 
3.0%
T 94
 
2.5%
P 90
 
2.4%
Other values (6) 270
 
7.1%
Common
ValueCountFrequency (%)
43
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1008
26.3%
E 471
12.3%
I 404
10.5%
N 357
 
9.3%
D 357
 
9.3%
V 357
 
9.3%
L 271
 
7.1%
C 114
 
3.0%
T 94
 
2.5%
P 90
 
2.3%
Other values (7) 313
 
8.2%

Calle
Categorical

Distinct251
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Memory size44.2 KiB
PAZ, GRAL. AV.
 
39
RIVADAVIA AV.
 
15
DEL LIBERTADOR AV.
 
13
ALBERDI, JUAN BAUTISTA AV.
 
11
AUTOPISTA 1 SUR PRESIDENTE ARTURO FRONDIZI
 
10
Other values (246)
473 

Length

Max length42
Median length30
Mean length15.634581
Min length4

Characters and Unicode

Total characters8771
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique159 ?
Unique (%)28.3%

Sample

1st rowPIEDRA BUENA AV.
2nd rowPAZ, GRAL. AV.
3rd rowENTRE RIOS AV.
4th rowSAN JUAN AV.
5th row27 DE FEBRERO AV.

Common Values

ValueCountFrequency (%)
PAZ, GRAL. AV. 39
 
7.0%
RIVADAVIA AV. 15
 
2.7%
DEL LIBERTADOR AV. 13
 
2.3%
ALBERDI, JUAN BAUTISTA AV. 11
 
2.0%
AUTOPISTA 1 SUR PRESIDENTE ARTURO FRONDIZI 10
 
1.8%
CORRIENTES AV. 10
 
1.8%
AUTOPISTA PERITO MORENO 10
 
1.8%
CORDOBA AV. 9
 
1.6%
AUTOPISTA 25 DE MAYO 8
 
1.4%
SAN MARTIN AV. 8
 
1.4%
Other values (241) 428
76.3%

Length

2023-09-07T23:01:07.467057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
av 328
 
22.2%
gral 62
 
4.2%
de 59
 
4.0%
paz 40
 
2.7%
autopista 40
 
2.7%
juan 31
 
2.1%
del 19
 
1.3%
san 19
 
1.3%
la 17
 
1.1%
moreno 16
 
1.1%
Other values (344) 849
57.4%

Most occurring characters

ValueCountFrequency (%)
A 1290
14.7%
919
 
10.5%
E 629
 
7.2%
R 613
 
7.0%
O 553
 
6.3%
I 475
 
5.4%
. 449
 
5.1%
V 418
 
4.8%
N 402
 
4.6%
L 399
 
4.5%
Other values (27) 2624
29.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7119
81.2%
Space Separator 919
 
10.5%
Other Punctuation 668
 
7.6%
Decimal Number 65
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1290
18.1%
E 629
 
8.8%
R 613
 
8.6%
O 553
 
7.8%
I 475
 
6.7%
V 418
 
5.9%
N 402
 
5.6%
L 399
 
5.6%
T 391
 
5.5%
S 303
 
4.3%
Other values (16) 1646
23.1%
Decimal Number
ValueCountFrequency (%)
1 17
26.2%
2 17
26.2%
5 10
15.4%
8 7
10.8%
7 6
 
9.2%
9 5
 
7.7%
4 3
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 449
67.2%
, 209
31.3%
? 10
 
1.5%
Space Separator
ValueCountFrequency (%)
919
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7119
81.2%
Common 1652
 
18.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1290
18.1%
E 629
 
8.8%
R 613
 
8.6%
O 553
 
7.8%
I 475
 
6.7%
V 418
 
5.9%
N 402
 
5.6%
L 399
 
5.6%
T 391
 
5.5%
S 303
 
4.3%
Other values (16) 1646
23.1%
Common
ValueCountFrequency (%)
919
55.6%
. 449
27.2%
, 209
 
12.7%
1 17
 
1.0%
2 17
 
1.0%
5 10
 
0.6%
? 10
 
0.6%
8 7
 
0.4%
7 6
 
0.4%
9 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8771
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1290
14.7%
919
 
10.5%
E 629
 
7.2%
R 613
 
7.0%
O 553
 
6.3%
I 475
 
5.4%
. 449
 
5.1%
V 418
 
4.8%
N 402
 
4.6%
L 399
 
4.5%
Other values (27) 2624
29.9%

Dirección Normalizada
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct527
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size53.8 KiB
PAZ, GRAL. AV. y BALBIN, RICARDO, DR. AV.
 
3
PAZ, GRAL. AV. y DE LOS CORRALES AV.
 
3
PAZ, GRAL. AV. y DEL LIBERTADOR AV.
 
3
27 DE FEBRERO AV. y ESCALADA AV.
 
3
ALCORTA, AMANCIO AV. y BONAVENA, OSCAR NATALIO
 
3
Other values (522)
546 

Length

Max length75
Median length52
Mean length30.094474
Min length8

Characters and Unicode

Total characters16883
Distinct characters50
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)89.1%

Sample

1st rowPIEDRA BUENA AV. y FERNANDEZ DE LA CRUZ, F., GRAL. AV.
2nd rowPAZ, GRAL. AV. y DE LOS CORRALES AV.
3rd rowENTRE RIOS AV. 2034
4th rowSAN JUAN AV. y SAENZ PEÑA, LUIS, PRES.
5th row27 DE FEBRERO AV. y ESCALADA AV.

Common Values

ValueCountFrequency (%)
PAZ, GRAL. AV. y BALBIN, RICARDO, DR. AV. 3
 
0.5%
PAZ, GRAL. AV. y DE LOS CORRALES AV. 3
 
0.5%
PAZ, GRAL. AV. y DEL LIBERTADOR AV. 3
 
0.5%
27 DE FEBRERO AV. y ESCALADA AV. 3
 
0.5%
ALCORTA, AMANCIO AV. y BONAVENA, OSCAR NATALIO 3
 
0.5%
INDEPENDENCIA AV. y CEVALLOS, VIRREY 3
 
0.5%
DEL LIBERTADOR AV. y RAMOS MEJIA, JOSE MARIA, DR. AV. 3
 
0.5%
SALGUERO, JERONIMO y RIVADAVIA AV. 2
 
0.4%
PAZ, GRAL. AV. y GOYENECHE, ROBERTO PARQUE AV. 2
 
0.4%
PAZ, GRAL. AV. y DONADO 2
 
0.4%
Other values (517) 534
95.2%

Length

2023-09-07T23:01:07.574011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
av 508
 
16.6%
y 435
 
14.2%
de 97
 
3.2%
gral 85
 
2.8%
paz 50
 
1.6%
autopista 44
 
1.4%
juan 40
 
1.3%
la 32
 
1.0%
del 28
 
0.9%
san 25
 
0.8%
Other values (650) 1709
56.0%

Most occurring characters

ValueCountFrequency (%)
2494
14.8%
A 2201
13.0%
E 1135
 
6.7%
R 1067
 
6.3%
O 949
 
5.6%
I 777
 
4.6%
N 733
 
4.3%
. 724
 
4.3%
L 718
 
4.3%
V 683
 
4.0%
Other values (40) 5402
32.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12308
72.9%
Space Separator 2494
 
14.8%
Other Punctuation 1097
 
6.5%
Decimal Number 510
 
3.0%
Lowercase Letter 444
 
2.6%
Initial Punctuation 15
 
0.1%
Close Punctuation 6
 
< 0.1%
Open Punctuation 6
 
< 0.1%
Control 2
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2201
17.9%
E 1135
 
9.2%
R 1067
 
8.7%
O 949
 
7.7%
I 777
 
6.3%
N 733
 
6.0%
L 718
 
5.8%
V 683
 
5.5%
T 577
 
4.7%
S 506
 
4.1%
Other values (18) 2962
24.1%
Decimal Number
ValueCountFrequency (%)
1 81
15.9%
2 69
13.5%
0 69
13.5%
5 63
12.4%
3 47
9.2%
4 42
8.2%
9 40
7.8%
7 35
6.9%
6 35
6.9%
8 29
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 724
66.0%
, 370
33.7%
2
 
0.2%
& 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
y 435
98.0%
e 9
 
2.0%
Space Separator
ValueCountFrequency (%)
2494
100.0%
Initial Punctuation
ValueCountFrequency (%)
15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Control
ValueCountFrequency (%)
 2
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12752
75.5%
Common 4131
 
24.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2201
17.3%
E 1135
 
8.9%
R 1067
 
8.4%
O 949
 
7.4%
I 777
 
6.1%
N 733
 
5.7%
L 718
 
5.6%
V 683
 
5.4%
T 577
 
4.5%
S 506
 
4.0%
Other values (20) 3406
26.7%
Common
ValueCountFrequency (%)
2494
60.4%
. 724
 
17.5%
, 370
 
9.0%
1 81
 
2.0%
2 69
 
1.7%
0 69
 
1.7%
5 63
 
1.5%
3 47
 
1.1%
4 42
 
1.0%
9 40
 
1.0%
Other values (10) 132
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16843
99.8%
None 23
 
0.1%
Punctuation 17
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2494
14.8%
A 2201
13.1%
E 1135
 
6.7%
R 1067
 
6.3%
O 949
 
5.6%
I 777
 
4.6%
N 733
 
4.4%
. 724
 
4.3%
L 718
 
4.3%
V 683
 
4.1%
Other values (34) 5362
31.8%
None
ValueCountFrequency (%)
à 19
82.6%
 2
 
8.7%
 1
 
4.3%
° 1
 
4.3%
Punctuation
ValueCountFrequency (%)
15
88.2%
2
 
11.8%

COMUNA
Real number (ℝ)

Distinct15
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3618538
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2023-09-07T23:01:07.651253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q311
95-th percentile15
Maximum15
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.4117424
Coefficient of variation (CV)0.59927058
Kurtosis-1.1295854
Mean7.3618538
Median Absolute Deviation (MAD)4
Skewness0.14480559
Sum4130
Variance19.463471
MonotonicityNot monotonic
2023-09-07T23:01:07.709858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 74
13.2%
4 62
11.1%
9 58
10.3%
8 50
8.9%
7 45
8.0%
15 39
 
7.0%
3 37
 
6.6%
13 31
 
5.5%
12 29
 
5.2%
14 26
 
4.6%
Other values (5) 110
19.6%
ValueCountFrequency (%)
1 74
13.2%
2 23
 
4.1%
3 37
6.6%
4 62
11.1%
5 20
 
3.6%
6 20
 
3.6%
7 45
8.0%
8 50
8.9%
9 58
10.3%
10 23
 
4.1%
ValueCountFrequency (%)
15 39
7.0%
14 26
4.6%
13 31
5.5%
12 29
5.2%
11 24
4.3%
10 23
 
4.1%
9 58
10.3%
8 50
8.9%
7 45
8.0%
6 20
 
3.6%

XY (CABA)
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct509
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Memory size56.5 KiB
Point (97801.31825511 99194.93911759)
 
3
Point (95832.05571093 95505.41641999)
 
3
Point (99418.67591727 99840.67619219)
 
3
Point (106817.01972475 101248.18030272)
 
3
Point (108069.68986814 104046.20018024)
 
3
Other values (504)
546 

Length

Max length39
Median length38
Mean length38.128342
Min length11

Characters and Unicode

Total characters21390
Distinct characters19
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique467 ?
Unique (%)83.2%

Sample

1st rowPoint (98896.78238426 93532.43437792)
2nd rowPoint (95832.05571093 95505.41641999)
3rd rowPoint (106684.29090040 99706.57687843)
4th rowPoint (106980.32827929 100752.16915795)
5th rowPoint (101721.59002217 93844.25656649)

Common Values

ValueCountFrequency (%)
Point (97801.31825511 99194.93911759) 3
 
0.5%
Point (95832.05571093 95505.41641999) 3
 
0.5%
Point (99418.67591727 99840.67619219) 3
 
0.5%
Point (106817.01972475 101248.18030272) 3
 
0.5%
Point (108069.68986814 104046.20018024) 3
 
0.5%
Point (99620.34936816 110483.29286598) 3
 
0.5%
Point (107903.91828587 98767.43091425) 3
 
0.5%
Point (96563.66494817 108815.73881056) 3
 
0.5%
Point (101721.59002217 93844.25656649) 3
 
0.5%
Point (105230.98315750 97613.97258373) 3
 
0.5%
Other values (499) 531
94.7%

Length

2023-09-07T23:01:07.779052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
point 561
33.3%
4
 
0.2%
110483.29286598 3
 
0.2%
97801.31825511 3
 
0.2%
97613.97258373 3
 
0.2%
105230.98315750 3
 
0.2%
101721.59002217 3
 
0.2%
108815.73881056 3
 
0.2%
96563.66494817 3
 
0.2%
98767.43091425 3
 
0.2%
Other values (1008) 1094
65.0%

Most occurring characters

ValueCountFrequency (%)
1 2023
 
9.5%
0 2012
 
9.4%
9 1796
 
8.4%
8 1396
 
6.5%
4 1382
 
6.5%
7 1365
 
6.4%
5 1353
 
6.3%
3 1325
 
6.2%
2 1286
 
6.0%
6 1281
 
6.0%
Other values (9) 6171
28.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15219
71.2%
Lowercase Letter 2244
 
10.5%
Other Punctuation 1122
 
5.2%
Space Separator 1122
 
5.2%
Open Punctuation 561
 
2.6%
Close Punctuation 561
 
2.6%
Uppercase Letter 561
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2023
13.3%
0 2012
13.2%
9 1796
11.8%
8 1396
9.2%
4 1382
9.1%
7 1365
9.0%
5 1353
8.9%
3 1325
8.7%
2 1286
8.4%
6 1281
8.4%
Lowercase Letter
ValueCountFrequency (%)
o 561
25.0%
t 561
25.0%
n 561
25.0%
i 561
25.0%
Other Punctuation
ValueCountFrequency (%)
. 1122
100.0%
Space Separator
ValueCountFrequency (%)
1122
100.0%
Open Punctuation
ValueCountFrequency (%)
( 561
100.0%
Close Punctuation
ValueCountFrequency (%)
) 561
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18585
86.9%
Latin 2805
 
13.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2023
10.9%
0 2012
10.8%
9 1796
9.7%
8 1396
7.5%
4 1382
7.4%
7 1365
7.3%
5 1353
7.3%
3 1325
 
7.1%
2 1286
 
6.9%
6 1281
 
6.9%
Other values (4) 3366
18.1%
Latin
ValueCountFrequency (%)
o 561
20.0%
t 561
20.0%
n 561
20.0%
i 561
20.0%
P 561
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2023
 
9.5%
0 2012
 
9.4%
9 1796
 
8.4%
8 1396
 
6.5%
4 1382
 
6.5%
7 1365
 
6.4%
5 1353
 
6.3%
3 1325
 
6.2%
2 1286
 
6.0%
6 1281
 
6.0%
Other values (9) 6171
28.8%

pos x
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct508
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Memory size42.2 KiB
-58.46743471
 
3
-58.37709334
 
3
-58.44451316
 
3
-58.37533517
 
3
-58.48727942
 
3
Other values (503)
546 

Length

Max length12
Median length12
Mean length11.960784
Min length1

Characters and Unicode

Total characters6710
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique465 ?
Unique (%)82.9%

Sample

1st row-58.47533969
2nd row-58.50877521
3rd row-58.39040293
4th row-58.38718297
5th row-58.44451316

Common Values

ValueCountFrequency (%)
-58.46743471 3
 
0.5%
-58.37709334 3
 
0.5%
-58.44451316 3
 
0.5%
-58.37533517 3
 
0.5%
-58.48727942 3
 
0.5%
-58.50877521 3
 
0.5%
-58.50073810 3
 
0.5%
-58.38896772 3
 
0.5%
-58.40623949 3
 
0.5%
-58.46963952 3
 
0.5%
Other values (498) 531
94.7%

Length

2023-09-07T23:01:07.844259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
58.46743471 3
 
0.5%
58.50073810 3
 
0.5%
58.37709334 3
 
0.5%
58.46963952 3
 
0.5%
58.38896772 3
 
0.5%
58.40623949 3
 
0.5%
58.50877521 3
 
0.5%
58.48727942 3
 
0.5%
58.37533517 3
 
0.5%
58.44451316 3
 
0.5%
Other values (498) 531
94.7%

Most occurring characters

ValueCountFrequency (%)
5 978
14.6%
8 919
13.7%
4 750
11.2%
. 561
8.4%
- 559
8.3%
3 467
7.0%
1 447
6.7%
2 425
6.3%
9 424
6.3%
6 409
6.1%
Other values (2) 771
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5590
83.3%
Other Punctuation 561
 
8.4%
Dash Punctuation 559
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 978
17.5%
8 919
16.4%
4 750
13.4%
3 467
8.4%
1 447
8.0%
2 425
7.6%
9 424
7.6%
6 409
7.3%
7 397
7.1%
0 374
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 561
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 559
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6710
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 978
14.6%
8 919
13.7%
4 750
11.2%
. 561
8.4%
- 559
8.3%
3 467
7.0%
1 447
6.7%
2 425
6.3%
9 424
6.3%
6 409
6.1%
Other values (2) 771
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 978
14.6%
8 919
13.7%
4 750
11.2%
. 561
8.4%
- 559
8.3%
3 467
7.0%
1 447
6.7%
2 425
6.3%
9 424
6.3%
6 409
6.1%
Other values (2) 771
11.5%

pos y
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct508
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Memory size42.2 KiB
-34.53476874
 
3
-34.64035082
 
3
-34.68475866
 
3
-34.59276462
 
3
-34.63652467
 
3
Other values (503)
546 

Length

Max length12
Median length12
Mean length11.960784
Min length1

Characters and Unicode

Total characters6710
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique465 ?
Unique (%)82.9%

Sample

1st row-34.68757022
2nd row-34.66977709
3rd row-34.63189362
4th row-34.62246630
5th row-34.68475866

Common Values

ValueCountFrequency (%)
-34.53476874 3
 
0.5%
-34.64035082 3
 
0.5%
-34.68475866 3
 
0.5%
-34.59276462 3
 
0.5%
-34.63652467 3
 
0.5%
-34.66977709 3
 
0.5%
-34.54979510 3
 
0.5%
-34.61799615 3
 
0.5%
-34.65076549 3
 
0.5%
-34.63070603 3
 
0.5%
Other values (498) 531
94.7%

Length

2023-09-07T23:01:07.910125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
34.53476874 3
 
0.5%
34.54979510 3
 
0.5%
34.64035082 3
 
0.5%
34.63070603 3
 
0.5%
34.61799615 3
 
0.5%
34.65076549 3
 
0.5%
34.66977709 3
 
0.5%
34.63652467 3
 
0.5%
34.59276462 3
 
0.5%
34.68475866 3
 
0.5%
Other values (498) 531
94.7%

Most occurring characters

ValueCountFrequency (%)
4 951
14.2%
3 931
13.9%
6 763
11.4%
5 589
8.8%
. 561
8.4%
- 559
8.3%
7 429
6.4%
9 411
6.1%
2 397
5.9%
8 382
5.7%
Other values (2) 737
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5590
83.3%
Other Punctuation 561
 
8.4%
Dash Punctuation 559
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 951
17.0%
3 931
16.7%
6 763
13.6%
5 589
10.5%
7 429
7.7%
9 411
7.4%
2 397
7.1%
8 382
6.8%
1 373
 
6.7%
0 364
 
6.5%
Other Punctuation
ValueCountFrequency (%)
. 561
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 559
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6710
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 951
14.2%
3 931
13.9%
6 763
11.4%
5 589
8.8%
. 561
8.4%
- 559
8.3%
7 429
6.4%
9 411
6.1%
2 397
5.9%
8 382
5.7%
Other values (2) 737
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 951
14.2%
3 931
13.9%
6 763
11.4%
5 589
8.8%
. 561
8.4%
- 559
8.3%
7 429
6.4%
9 411
6.1%
2 397
5.9%
8 382
5.7%
Other values (2) 737
11.0%

PARTICIPANTES
Categorical

Distinct31
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size42.4 KiB
PEATON-PASAJEROS
92 
MOTO-AUTO
74 
MOTO-CARGAS
64 
PEATON-AUTO
63 
MOTO-OBJETO FIJO
36 
Other values (26)
232 

Length

Max length19
Median length18
Mean length12.483066
Min length8

Characters and Unicode

Total characters7003
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)2.0%

Sample

1st rowMOTO-AUTO
2nd rowAUTO-PASAJEROS
3rd rowMOTO-AUTO
4th rowMOTO-PASAJEROS
5th rowMOTO-OBJETO FIJO

Common Values

ValueCountFrequency (%)
PEATON-PASAJEROS 92
16.4%
MOTO-AUTO 74
13.2%
MOTO-CARGAS 64
11.4%
PEATON-AUTO 63
11.2%
MOTO-OBJETO FIJO 36
 
6.4%
MOTO-PASAJEROS 35
 
6.2%
PEATON-CARGAS 34
 
6.1%
PEATON-MOTO 29
 
5.2%
AUTO-AUTO 23
 
4.1%
AUTO-OBJETO FIJO 16
 
2.9%
Other values (21) 95
16.9%

Length

2023-09-07T23:01:07.980307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
peaton-pasajeros 92
15.0%
moto-auto 74
12.1%
moto-cargas 64
10.4%
peaton-auto 63
10.3%
fijo 53
8.6%
moto-objeto 36
 
5.9%
moto-pasajeros 35
 
5.7%
peaton-cargas 34
 
5.5%
peaton-moto 29
 
4.7%
auto-auto 23
 
3.7%
Other values (22) 111
18.1%

Most occurring characters

ValueCountFrequency (%)
O 1314
18.8%
A 1037
14.8%
T 827
11.8%
- 546
7.8%
E 470
 
6.7%
S 426
 
6.1%
P 386
 
5.5%
M 291
 
4.2%
R 280
 
4.0%
J 255
 
3.6%
Other values (10) 1171
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6404
91.4%
Dash Punctuation 546
 
7.8%
Space Separator 53
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1314
20.5%
A 1037
16.2%
T 827
12.9%
E 470
 
7.3%
S 426
 
6.7%
P 386
 
6.0%
M 291
 
4.5%
R 280
 
4.4%
J 255
 
4.0%
U 246
 
3.8%
Other values (8) 872
13.6%
Dash Punctuation
ValueCountFrequency (%)
- 546
100.0%
Space Separator
ValueCountFrequency (%)
53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6404
91.4%
Common 599
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1314
20.5%
A 1037
16.2%
T 827
12.9%
E 470
 
7.3%
S 426
 
6.7%
P 386
 
6.0%
M 291
 
4.5%
R 280
 
4.4%
J 255
 
4.0%
U 246
 
3.8%
Other values (8) 872
13.6%
Common
ValueCountFrequency (%)
- 546
91.2%
53
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 1314
18.8%
A 1037
14.8%
T 827
11.8%
- 546
7.8%
E 470
 
6.7%
S 426
 
6.1%
P 386
 
5.5%
M 291
 
4.2%
R 280
 
4.0%
J 255
 
3.6%
Other values (10) 1171
16.7%

VICTIMA
Categorical

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size38.4 KiB
MOTO
236 
PEATON
226 
AUTO
63 
BICICLETA
26 
CARGAS
 
6
Other values (2)
 
4

Length

Max length9
Median length4
Mean length5.087344
Min length4

Characters and Unicode

Total characters2854
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowMOTO
2nd rowAUTO
3rd rowMOTO
4th rowMOTO
5th rowMOTO

Common Values

ValueCountFrequency (%)
MOTO 236
42.1%
PEATON 226
40.3%
AUTO 63
 
11.2%
BICICLETA 26
 
4.6%
CARGAS 6
 
1.1%
PASAJEROS 3
 
0.5%
MOVIL 1
 
0.2%

Length

2023-09-07T23:01:08.048115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-07T23:01:08.121324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
moto 236
42.1%
peaton 226
40.3%
auto 63
 
11.2%
bicicleta 26
 
4.6%
cargas 6
 
1.1%
pasajeros 3
 
0.5%
movil 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
O 765
26.8%
T 551
19.3%
A 333
11.7%
E 255
 
8.9%
M 237
 
8.3%
P 229
 
8.0%
N 226
 
7.9%
U 63
 
2.2%
C 58
 
2.0%
I 53
 
1.9%
Other values (7) 84
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2854
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 765
26.8%
T 551
19.3%
A 333
11.7%
E 255
 
8.9%
M 237
 
8.3%
P 229
 
8.0%
N 226
 
7.9%
U 63
 
2.2%
C 58
 
2.0%
I 53
 
1.9%
Other values (7) 84
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 2854
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 765
26.8%
T 551
19.3%
A 333
11.7%
E 255
 
8.9%
M 237
 
8.3%
P 229
 
8.0%
N 226
 
7.9%
U 63
 
2.2%
C 58
 
2.0%
I 53
 
1.9%
Other values (7) 84
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 765
26.8%
T 551
19.3%
A 333
11.7%
E 255
 
8.9%
M 237
 
8.3%
P 229
 
8.0%
N 226
 
7.9%
U 63
 
2.2%
C 58
 
2.0%
I 53
 
1.9%
Other values (7) 84
 
2.9%

ACUSADO
Categorical

Distinct9
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
AUTO
170 
PASAJEROS
146 
CARGAS
122 
OBJETO FIJO
53 
MOTO
45 
Other values (4)
25 

Length

Max length11
Median length9
Mean length6.5490196
Min length4

Characters and Unicode

Total characters3674
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowAUTO
2nd rowPASAJEROS
3rd rowAUTO
4th rowPASAJEROS
5th rowOBJETO FIJO

Common Values

ValueCountFrequency (%)
AUTO 170
30.3%
PASAJEROS 146
26.0%
CARGAS 122
21.7%
OBJETO FIJO 53
 
9.4%
MOTO 45
 
8.0%
MULTIPLE 15
 
2.7%
BICICLETA 5
 
0.9%
OTRO 4
 
0.7%
TREN 1
 
0.2%

Length

2023-09-07T23:01:08.193460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-07T23:01:08.272683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
auto 170
27.7%
pasajeros 146
23.8%
cargas 122
19.9%
objeto 53
 
8.6%
fijo 53
 
8.6%
moto 45
 
7.3%
multiple 15
 
2.4%
bicicleta 5
 
0.8%
otro 4
 
0.7%
tren 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 711
19.4%
O 573
15.6%
S 414
11.3%
T 293
8.0%
R 273
 
7.4%
J 252
 
6.9%
E 220
 
6.0%
U 185
 
5.0%
P 161
 
4.4%
C 132
 
3.6%
Other values (8) 460
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3621
98.6%
Space Separator 53
 
1.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 711
19.6%
O 573
15.8%
S 414
11.4%
T 293
8.1%
R 273
 
7.5%
J 252
 
7.0%
E 220
 
6.1%
U 185
 
5.1%
P 161
 
4.4%
C 132
 
3.6%
Other values (7) 407
11.2%
Space Separator
ValueCountFrequency (%)
53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3621
98.6%
Common 53
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 711
19.6%
O 573
15.8%
S 414
11.4%
T 293
8.1%
R 273
 
7.5%
J 252
 
7.0%
E 220
 
6.1%
U 185
 
5.1%
P 161
 
4.4%
C 132
 
3.6%
Other values (7) 407
11.2%
Common
ValueCountFrequency (%)
53
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3674
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 711
19.4%
O 573
15.6%
S 414
11.3%
T 293
8.0%
R 273
 
7.4%
J 252
 
6.9%
E 220
 
6.0%
U 185
 
5.0%
P 161
 
4.4%
C 132
 
3.6%
Other values (8) 460
12.5%

ROL
Categorical

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size42.6 KiB
CONDUCTOR
260 
PEATON
226 
PASAJERO_ACOMPAÑANTE
49 
CICLISTA
 
26

Length

Max length20
Median length9
Mean length8.7058824
Min length6

Characters and Unicode

Total characters4884
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCONDUCTOR
2nd rowCONDUCTOR
3rd rowCONDUCTOR
4th rowCONDUCTOR
5th rowCONDUCTOR

Common Values

ValueCountFrequency (%)
CONDUCTOR 260
46.3%
PEATON 226
40.3%
PASAJERO_ACOMPAÑANTE 49
 
8.7%
CICLISTA 26
 
4.6%

Length

2023-09-07T23:01:08.353696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-07T23:01:08.425964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
conductor 260
46.3%
peaton 226
40.3%
pasajero_acompañante 49
 
8.7%
ciclista 26
 
4.6%

Most occurring characters

ValueCountFrequency (%)
O 844
17.3%
C 621
12.7%
T 561
11.5%
N 535
11.0%
A 497
10.2%
P 324
 
6.6%
E 324
 
6.6%
R 309
 
6.3%
U 260
 
5.3%
D 260
 
5.3%
Other values (7) 349
7.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4835
99.0%
Connector Punctuation 49
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 844
17.5%
C 621
12.8%
T 561
11.6%
N 535
11.1%
A 497
10.3%
P 324
 
6.7%
E 324
 
6.7%
R 309
 
6.4%
U 260
 
5.4%
D 260
 
5.4%
Other values (6) 300
 
6.2%
Connector Punctuation
ValueCountFrequency (%)
_ 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4835
99.0%
Common 49
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 844
17.5%
C 621
12.8%
T 561
11.6%
N 535
11.1%
A 497
10.3%
P 324
 
6.7%
E 324
 
6.7%
R 309
 
6.4%
U 260
 
5.4%
D 260
 
5.4%
Other values (6) 300
 
6.2%
Common
ValueCountFrequency (%)
_ 49
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4835
99.0%
None 49
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 844
17.5%
C 621
12.8%
T 561
11.6%
N 535
11.1%
A 497
10.3%
P 324
 
6.7%
E 324
 
6.7%
R 309
 
6.4%
U 260
 
5.4%
D 260
 
5.4%
Other values (6) 300
 
6.2%
None
ValueCountFrequency (%)
Ñ 49
100.0%

SEXO
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size40.4 KiB
MASCULINO
425 
FEMENINO
136 

Length

Max length9
Median length9
Mean length8.7575758
Min length8

Characters and Unicode

Total characters4913
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMASCULINO
2nd rowMASCULINO
3rd rowMASCULINO
4th rowMASCULINO
5th rowMASCULINO

Common Values

ValueCountFrequency (%)
MASCULINO 425
75.8%
FEMENINO 136
 
24.2%

Length

2023-09-07T23:01:08.490015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-07T23:01:08.553303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
masculino 425
75.8%
femenino 136
 
24.2%

Most occurring characters

ValueCountFrequency (%)
N 697
14.2%
M 561
11.4%
I 561
11.4%
O 561
11.4%
A 425
8.7%
S 425
8.7%
C 425
8.7%
U 425
8.7%
L 425
8.7%
E 272
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4913
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 697
14.2%
M 561
11.4%
I 561
11.4%
O 561
11.4%
A 425
8.7%
S 425
8.7%
C 425
8.7%
U 425
8.7%
L 425
8.7%
E 272
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 4913
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 697
14.2%
M 561
11.4%
I 561
11.4%
O 561
11.4%
A 425
8.7%
S 425
8.7%
C 425
8.7%
U 425
8.7%
L 425
8.7%
E 272
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 697
14.2%
M 561
11.4%
I 561
11.4%
O 561
11.4%
A 425
8.7%
S 425
8.7%
C 425
8.7%
U 425
8.7%
L 425
8.7%
E 272
 
5.5%

EDAD
Real number (ℝ)

Distinct84
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.237077
Minimum1
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2023-09-07T23:01:08.620006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19
Q127
median38
Q357
95-th percentile81
Maximum95
Range94
Interquartile range (IQR)30

Descriptive statistics

Standard deviation20.228316
Coefficient of variation (CV)0.46784652
Kurtosis-0.6597436
Mean43.237077
Median Absolute Deviation (MAD)13
Skewness0.6195803
Sum24256
Variance409.18477
MonotonicityNot monotonic
2023-09-07T23:01:08.699393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 19
 
3.4%
30 19
 
3.4%
29 19
 
3.4%
23 16
 
2.9%
28 16
 
2.9%
26 15
 
2.7%
32 14
 
2.5%
24 14
 
2.5%
21 14
 
2.5%
39 13
 
2.3%
Other values (74) 402
71.7%
ValueCountFrequency (%)
1 1
 
0.2%
4 2
0.4%
5 1
 
0.2%
10 1
 
0.2%
11 1
 
0.2%
12 1
 
0.2%
13 1
 
0.2%
15 2
0.4%
16 4
0.7%
17 3
0.5%
ValueCountFrequency (%)
95 1
 
0.2%
92 1
 
0.2%
91 2
 
0.4%
88 1
 
0.2%
87 5
0.9%
86 2
 
0.4%
85 1
 
0.2%
84 5
0.9%
83 3
0.5%
82 5
0.9%
Distinct508
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
Minimum2016-01-01 00:00:00
Maximum2022-01-03 00:00:00
2023-09-07T23:01:08.781730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:08.867200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

POBLACIÓN AL AÑO
Real number (ℝ)

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3069230
Minimum3059122
Maximum3078836
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2023-09-07T23:01:08.943121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3059122
5-th percentile3059122
Q13063728
median3068043
Q33075646
95-th percentile3078836
Maximum3078836
Range19714
Interquartile range (IQR)11918

Descriptive statistics

Standard deviation6482.8345
Coefficient of variation (CV)0.0021122022
Kurtosis-1.1453286
Mean3069230
Median Absolute Deviation (MAD)4315
Skewness-0.0028540033
Sum1.721838 × 109
Variance42027143
MonotonicityIncreasing
2023-09-07T23:01:08.998235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3068043 125
22.3%
3063728 109
19.4%
3078836 91
16.2%
3072029 88
15.7%
3059122 80
14.3%
3075646 68
12.1%
ValueCountFrequency (%)
3059122 80
14.3%
3063728 109
19.4%
3068043 125
22.3%
3072029 88
15.7%
3075646 68
12.1%
3078836 91
16.2%
ValueCountFrequency (%)
3078836 91
16.2%
3075646 68
12.1%
3072029 88
15.7%
3068043 125
22.3%
3063728 109
19.4%
3059122 80
14.3%

Interactions

2023-09-07T23:01:05.058642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:01.763322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:02.218474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:02.735779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:03.267901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:03.753953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:04.525210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:05.138354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:01.827223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:02.280198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:02.820369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:03.340366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:03.829149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:04.593899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:05.214427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:01.889495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:02.345771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:02.900906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:03.406199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:04.197773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:04.660463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:05.291083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:01.952313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:02.421098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:02.976344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:03.476821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:04.262430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:04.734457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:05.367676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:02.011168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:02.497739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:03.044868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:03.541857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:04.322953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:04.807854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:05.446430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:02.087660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:02.576395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:03.117149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:03.613702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:04.385659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:04.894065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:05.521435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:02.150102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:02.647097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:03.187061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:03.678798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:04.446553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-09-07T23:01:04.971121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-09-07T23:01:09.070993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
AAAAMMDDHHCOMUNAEDADPOBLACIÓN AL AÑOTIPO_DE_CALLEPARTICIPANTESVICTIMAACUSADOROLSEXO
AAAA1.0000.030-0.023-0.0230.0320.0771.0000.0540.0790.0140.0960.0390.075
MM0.0301.000-0.0110.0510.0470.0430.0300.0310.0470.0360.0000.0780.000
DD-0.023-0.0111.000-0.071-0.004-0.025-0.0230.0000.0280.0000.0230.0000.025
HH-0.0230.051-0.0711.000-0.0490.184-0.0230.0000.1440.1160.1260.1470.180
COMUNA0.0320.047-0.004-0.0491.000-0.0710.0320.2540.0770.0880.0900.0760.000
EDAD0.0770.043-0.0250.184-0.0711.0000.0770.1070.1800.2350.0860.3110.322
POBLACIÓN AL AÑO1.0000.030-0.023-0.0230.0320.0771.0000.0540.0790.0140.0960.0390.075
TIPO_DE_CALLE0.0540.0310.0000.0000.2540.1070.0541.0000.1900.1250.1240.1340.050
PARTICIPANTES0.0790.0470.0280.1440.0770.1800.0790.1901.0000.9700.9800.8090.313
VICTIMA0.0140.0360.0000.1160.0880.2350.0140.1250.9701.0000.1740.8380.320
ACUSADO0.0960.0000.0230.1260.0900.0860.0960.1240.9800.1741.0000.2580.060
ROL0.0390.0780.0000.1470.0760.3110.0390.1340.8090.8380.2581.0000.402
SEXO0.0750.0000.0250.1800.0000.3220.0750.0500.3130.3200.0600.4021.000

Missing values

2023-09-07T23:01:05.668295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-07T23:01:05.881961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDN_VICTIMASFECHAAAAAMMDDHORAHHLUGAR_DEL_HECHOTIPO_DE_CALLECalleDirección NormalizadaCOMUNAXY (CABA)pos xpos yPARTICIPANTESVICTIMAACUSADOROLSEXOEDADFECHA_FALLECIMIENTOPOBLACIÓN AL AÑO
02016-000112016-01-0120161104:00:004AV PIEDRA BUENA Y AV FERNANDEZ DE LA CRUZAVENIDAPIEDRA BUENA AV.PIEDRA BUENA AV. y FERNANDEZ DE LA CRUZ, F., GRAL. AV.8Point (98896.78238426 93532.43437792)-58.47533969-34.68757022MOTO-AUTOMOTOAUTOCONDUCTORMASCULINO192016-01-01 00:00:003059122
12016-000212016-01-0220161201:15:001AV GRAL PAZ Y AV DE LOS CORRALESGRAL PAZPAZ, GRAL. AV.PAZ, GRAL. AV. y DE LOS CORRALES AV.9Point (95832.05571093 95505.41641999)-58.50877521-34.66977709AUTO-PASAJEROSAUTOPASAJEROSCONDUCTORMASCULINO702016-01-02 00:00:003059122
22016-000312016-01-0320161307:00:007AV ENTRE RIOS 2034AVENIDAENTRE RIOS AV.ENTRE RIOS AV. 20341Point (106684.29090040 99706.57687843)-58.39040293-34.63189362MOTO-AUTOMOTOAUTOCONDUCTORMASCULINO302016-01-03 00:00:003059122
42016-000512016-01-21201612105:20:005AV SAN JUAN Y PRESIDENTE LUIS SAENZ PEÑAAVENIDASAN JUAN AV.SAN JUAN AV. y SAENZ PEÑA, LUIS, PRES.1Point (106980.32827929 100752.16915795)-58.38718297-34.62246630MOTO-PASAJEROSMOTOPASAJEROSCONDUCTORMASCULINO292016-02-01 00:00:003059122
52016-000812016-01-24201612418:30:0018AV 27 DE FEBRERO Y AV ESCALADAAVENIDA27 DE FEBRERO AV.27 DE FEBRERO AV. y ESCALADA AV.8Point (101721.59002217 93844.25656649)-58.44451316-34.68475866MOTO-OBJETO FIJOMOTOOBJETO FIJOCONDUCTORMASCULINO302016-01-24 00:00:003059122
62016-000912016-01-24201612419:10:0019NOGOYA Y JOAQUIN V. GONZALESCALLENOGOYANOGOYA y GONZALEZ, JOAQUIN V.11Point (96545.87592078 102330.67262199)-58.50095869-34.60825440MOTO-AUTOMOTOAUTOPASAJERO_ACOMPAÑANTEMASCULINO292016-01-26 00:00:003059122
72016-001012016-01-29201612915:20:0015AV GENERAL PAZ Y AV DE LOS CORRALESGRAL PAZPAZ, GRAL. AV.PAZ, GRAL. AV. y DE LOS CORRALES AV.9Point (95832.05571093 95505.41641999)-58.50877521-34.66977709MOTO-AUTOMOTOAUTOCONDUCTORMASCULINO182016-01-29 00:00:003059122
82016-001212016-02-0820162801:20:001AV BELGRANO Y BERNARDO DE IRIGOYENAVENIDABELGRANO AV.BELGRANO AV. e IRIGOYEN, BERNARDO DE1Point (107595.35084333 101797.50052813)-58.38048577-34.61303893MOTO-CARGASMOTOCARGASCONDUCTORMASCULINO222016-02-08 00:00:003059122
92016-001312016-02-10201621011:30:0011AV ENTRE RIOS 1366AVENIDAENTRE RIOS AV.ENTRE RIOS AV. 13661Point (106616.41069662 100496.44662323)-58.39114932-34.62477387PEATON-AUTOPEATONAUTOPEATONMASCULINO162016-02-10 00:00:003059122
102016-001512016-02-14201621405:14:005AV SCALABRINI ORTIZ Y VERAAVENIDASCALABRINI ORTIZ, RAUL AV.SCALABRINI ORTIZ, RAUL AV. y VERA15Point (102357.43746828 103343.52002839)-58.43760020-34.59912758PEATON-AUTOPEATONAUTOPEATONFEMENINO162016-02-14 00:00:003059122
IDN_VICTIMASFECHAAAAAMMDDHORAHHLUGAR_DEL_HECHOTIPO_DE_CALLECalleDirección NormalizadaCOMUNAXY (CABA)pos xpos yPARTICIPANTESVICTIMAACUSADOROLSEXOEDADFECHA_FALLECIMIENTOPOBLACIÓN AL AÑO
7072021-008812021-12-01202112115:40:0015AV. MOROE Y 3 DE FEBREROCALLEMONROEMONROE y 3 DE FEBRERO13Point (100732.60222975 108177.68150062)-58.45531707-34.55555257MOTO-AUTOMOTOAUTOCONDUCTORMASCULINO452021-12-07 00:00:003078836
7082021-008912021-12-02202112201:10:001AV. GAONA 3655AVENIDAGAONA AV.GAONA AV. 365511Point (98804.41713890 100872.30706871)-58.47633683-34.62140594MOTO-AUTOMOTOAUTOCONDUCTORMASCULINO412021-12-11 00:00:003078836
7092021-009012021-12-102021121011:45:0011AV. 9 DE JULIO Y LAVALLEAVENIDA9 DE JULIO AV.9 DE JULIO AV. y LAVALLE1Point (107467.87595573 102960.02837514)-58.38188582-34.60256036PEATON-PASAJEROSPEATONPASAJEROSPEATONMASCULINO732022-01-03 00:00:003078836
7102021-009112021-12-112021121123:00:0023BAIGORRIA Y VICTOR HUGOCALLEBAIGORRIABAIGORRIA y HUGO, VICTOR10Point (94810.03686085 100710.80080255)-58.51989389-34.62284918MOTO-AUTOMOTOAUTOCONDUCTORMASCULINO242021-12-11 00:00:003078836
7112021-009212021-12-122021121206:20:006AV. RIVADAVIA Y AV. PUEYRREDONAVENIDARIVADAVIA AV.RIVADAVIA AV. y PUEYRREDON AV.3Point (105258.35368554 102122.93231400)-58.40596860-34.61011987PEATON-AUTOPEATONAUTOPEATONFEMENINO502021-12-12 00:00:003078836
7122021-009312021-12-132021121317:10:0017AV. RIESTRA Y MOMAVENIDARIESTRA AV.RIESTRA AV. y MOM7Point (102728.60090138 98186.24929177)-58.43353773-34.64561636MOTO-AUTOMOTOAUTOPASAJERO_ACOMPAÑANTEFEMENINO182021-12-18 00:00:003078836
7132021-009412021-12-202021122001:10:001AU DELLEPIANE Y LACARRAAUTOPISTADELLEPIANE, LUIS, TTE. GRAL.DELLEPIANE, LUIS, TTE. GRAL. y LACARRA AV.9Point (99624.29795829 97569.69801131)-58.46739825-34.65117757MOTO-AUTOMOTOAUTOPASAJERO_ACOMPAÑANTEFEMENINO432021-12-20 00:00:003078836
7142021-009512021-12-302021123000:43:000AV. GAONA Y TERRADAAVENIDAGAONA AV.GAONA AV. y TERRADA11Point (99116.45492358 101045.23284826)-58.47293407-34.61984745MOTO-CARGASMOTOCARGASCONDUCTORMASCULINO272022-01-02 00:00:003078836
7152021-009612021-12-152021121510:30:0010AV. EVA PERON 4071AVENIDAPERON, EVA AV.PERON, EVA AV. 40719Point (99324.54463985 97676.26932409)-58.47066794-34.65021673AUTO-CARGASAUTOCARGASCONDUCTORMASCULINO602021-12-20 00:00:003078836
7162021-009712021-11-182021111806:10:006PADRE CARLOS MUJICA 709CALLEPADRE CARLOS MUJICAPADRE CARLOS MUGICA 7091Point (107664.16647795 104708.63962087)-58.37976155-34.58679619BICICLETA-AUTOBICICLETAAUTOCICLISTAMASCULINO532021-11-19 00:00:003078836